Dynamic scene recognition based on improved visual vocabulary model

In this paper, we present a scene recognition framework, which could process the images and recognize the scene in the images. We demonstrate and evaluate the performance of our system on a dataset of Oxford typical landmarks. In this paper, we put forward a novel method of local k-meriod for building a vocabulary and introduce a novel quantization method of soft-assignment based on the Gaussian mixture model. Then we also introduced the Gaussian model in order to classify the images into different scenes by calculating the probability of whether an image belongs to the scene, and we further improve the model by drawing out the consistent features and filtering out the noise features. Our experiment proves that these methods actually improve the classifying performance.